Sample Sizes for Comparing SUS Scores

Microsoft Word is a widely used word processing program, part of the Microsoft Office suite of programs. While its dominance has been challenged recently by Google Docs, Word still leads on the features list, providing many features that Google’s offering lacks. But adding features can also add to bloat, making common tasks harder as users

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Eight Laws of Statistics

Statistics doesn’t have a Magna Carta, constitution, or bill of rights to enumerate laws, guiding principles, or limits of power. There have been attempts to articulate credos for statistical practice. Two of the most enduring ones are based on the work by Robert P. Abelson, a former statistical professor at Yale. If Abelson wasn’t the

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Sample Sizes for a SUS Score

Despite its age and the availability of other UX measures such as the UX-Lite™ and SUPR-Q®, the ten-item System Usability Scale (SUS) is still a very popular measure. It’s used widely in benchmark tests of software products to generate an overall score of perceived usability. We regularly collect SUS scores for dozens of consumer and

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From Statistical to Practical Significance

Hypothesis testing is one of the most common frameworks for making decisions with data in both scientific and industrial contexts. But this statistical framework, formally called Null Hypothesis Statistics Testing (NHST), can be confusing (and controversial). In an earlier article, we showed how to use the core framework of statistical hypothesis testing: you start with

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Statistical Hypothesis Testing: What Can Go Wrong?

Making decisions with data inevitably means working with statistics and one of its most common frameworks: Null Hypothesis Significance Testing (NHST). Hypothesis testing can be confusing (and controversial), so in an earlier article we introduced the core framework of statistical hypothesis testing in four steps: Define the null hypothesis (H0). This is the hypothesis that

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How to Statistically Compare Two Net Promoter Scores

When we wrote Quantifying the User Experience, we put confidence intervals before tests of statistical significance. We generally find fluency with confidence intervals to be easier to achieve and of more value than with formal hypothesis testing. We also teach confidence intervals in our workshops on statistical methods. Most people, even non-researchers, have been exposed

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Confidence Intervals for Net Promoter Scores

The Net Promoter Score (NPS) is a widely used metric, but it can be tricky to work with statistically. One of the first statistical steps we recommend that researchers take is to add confidence intervals around their metrics. Confidence intervals provide a good visualization of how precise estimates from samples are. They are particularly helpful

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How Confident Do You Need to be in Your Research?

Every estimate we make from a sample of customer data contains error. Confidence intervals tell us how much faith we can have in our estimates. Confidence intervals quantify the most likely range for the unknown value we’re estimating. For example, if we observe 27 out of 30 users (90%) completing a task, we can be

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How to Compute a Confidence Interval in 5 Easy Steps

Confidence intervals are your frenemies. They are one of the most useful statistical techniques you can apply to customer data. At the same time they can be perplexing and cumbersome. But confidence intervals provide an essential understanding of how much faith we can have in our sample estimates, from any sample size, from 2 to

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10 Things to know about Confidence Intervals

You don’t need a PhD in statistics to understand and use confidence intervals. Because we almost always sample a fraction of the users from a larger population, there is uncertainty in our estimates. Confidence intervals are an excellent way of understanding the role of sampling error in the averages and percentages that are ubiquitous in

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